flair.datasets.text_text.OpusParallelCorpus#

class flair.datasets.text_text.OpusParallelCorpus(dataset, l1, l2, use_tokenizer=True, max_tokens_per_doc=-1, max_chars_per_doc=-1, in_memory=True, **corpusargs)View on GitHub#

Bases: ParallelTextCorpus

__init__(dataset, l1, l2, use_tokenizer=True, max_tokens_per_doc=-1, max_chars_per_doc=-1, in_memory=True, **corpusargs)View on GitHub#

Instantiates a Parallel Corpus from OPUS.

see http://opus.nlpl.eu/

Parameters:

  • dataset (str) – Name of the dataset (one of “tatoeba”)

  • l1 (str) – Language code of first language in pair (“en”, “de”, etc.)

  • l2 (str) – Language code of second language in pair (“en”, “de”, etc.)

  • use_tokenizer (Union[bool, Tokenizer]) – You can optionally specify a custom tokenizer to split the text into tokens. By default, flair.tokenization.SegtokTokenizer will be used. If use_tokenizer is set to False, flair.tokenization.SpaceTokenizer will be used instead.

  • max_tokens_per_doc – If set, shortens sentences to this maximum number of tokens

  • max_chars_per_doc – If set, shortens sentences to this maximum number of characters

  • in_memory (bool) – If True, keeps dataset fully in memory

Methods

__init__(dataset, l1, l2[, use_tokenizer, ...])

Instantiates a Parallel Corpus from OPUS.

add_label_noise(label_type, labels[, ...])

Generates uniform label noise distribution in the chosen dataset split.

downsample([percentage, downsample_train, ...])

Randomly downsample the corpus to the given percentage (by removing data points).

filter_empty_sentences()

A method that filters all sentences consisting of 0 tokens.

filter_long_sentences(max_charlength)

A method that filters all sentences for which the plain text is longer than a specified number of characters.

get_all_sentences()

Returns all sentences (spanning all three splits) in the Corpus.

get_label_distribution()

Counts occurrences of each label in the corpus and returns them as a dictionary object.

is_in_memory()

make_label_dictionary(label_type[, ...])

Creates a dictionary of all labels assigned to the sentences in the corpus.

make_tag_dictionary(tag_type)

Create a tag dictionary of a given label type.

make_vocab_dictionary([max_tokens, min_freq])

Creates a Dictionary of all tokens contained in the corpus.

obtain_statistics([label_type, pretty_print])

Print statistics about the corpus, including the length of the sentences and the labels in the corpus.

Attributes

dev

The dev split as a torch.utils.data.Dataset object.

test

The test split as a torch.utils.data.Dataset object.

train

The training split as a torch.utils.data.Dataset object.